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Band selection is a very important hyperspectral image preprocessing before using data. A novel bands selection method for hyperspectral data based on convolutional neural network (CNN) is proposed in this paper. In this way, we use a custom one-dimensional CNN to train the hyperspectral data to obtain a well-trained model. After testing band combinations, we use the model to obtain the test precision...
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling,...
While the intrusion detection system in network is making great progress, it is also facing great challenges. The applications of data mining technique in computer security field improve the development of EDS. It is necessary to classify the attack degrees in IDS and use it in IDS by data mining. In spite of IDS can detect the attack activities in network, the result is uncertain. To describe the...
A new inductive transfer-learning algorithm called NEDRT is presented in this paper in order to improve the classification accuracy of a domain task by using the knowledge learned from labeled data generated from a different domain. NEDRT introduces a novel error function for a constructed neural network by summing a weighted squared difference between the real output and the neural network output...
Energy efficiency is an important issue in wireless sensor networks. One available power saving strategy is having only a portion of nodes work, but this would always compromise data quality as a result. In this paper, we propose an adaptive nodes scheduling approach (ADNS) to conserve energy while maintaining the overall data quality. ADNS selects a subset of nodes to be active and puts the others...
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